The present embodiments relate to medical imaging of three-dimensional (3D) scans. Data representing a volume is rendered for visualization. Due to the many different scan settings and patient variability, renderings for different patients or at different times appear different.
In an effort to reduce variability, most existing medical rendering provides a set of static visualization presets for specific workflows or diagnostic contexts. Even with static presets, the rendering may require manual adjustment and may not provide consistent rendered results. Semi-automatic machine learning approaches have been used to provide transfer function design in traditional volume rendering (e.g., projection or alpha blending), but may still not sufficiently reduce variability.
Variability is particularly troublesome for physically-based volume rendering, which relies on the physical simulation of light propagation (e.g., unbiased path tracing). Physically-based visualization techniques produce global illumination effects in computer-generated graphics that mimic the real-world interaction of light with various 3D objects or tissues. This results in physically plausible images that are often easier for the human brain to interpret when compared to the more analytical images from traditional rendering. For example, subtle ambient occlusions, shadows, and color bleeding provide important depth cues for the spatial understanding of 3D relationships between structures in a single 2D image, whereas simpler visualization techniques may require additional interaction with the viewing parameters (e.g., moving the virtual camera around the 3D data) to obtain the same spatial information from the image parallax.
These physically-based visualization techniques are more sensitive to changes in the classification or the interpretation of the underlying medical data. As a result, small changes to the rendering parameters may have a more pronounced impact on the perception and interpretation of 3D structures in the final image as compared to the traditional volume rendering techniques based on compositing of classified voxels along viewing rays. Obtaining very high quality reproducible images with diagnostic significance is then partially at the user's discretion. While existing techniques for providing visualization presets can help, the workflow is not fully automated and the resulting rendered images may not have consistent quantitative properties (color, hue, reflectance, etc.) across datasets.